{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "skilled-perception",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "certain-bobby",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seriesId</th>\n",
       "      <th>sum2020</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sg9550</td>\n",
       "      <td>76912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sg3524</td>\n",
       "      <td>130906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sg4313</td>\n",
       "      <td>173188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sg21448</td>\n",
       "      <td>5313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sg22543</td>\n",
       "      <td>46973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2232</th>\n",
       "      <td>sg28153</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2233</th>\n",
       "      <td>sg28154</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2234</th>\n",
       "      <td>sg20442</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2235</th>\n",
       "      <td>sg1777</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2236</th>\n",
       "      <td>sg1778</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2237 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     seriesId sum2020\n",
       "0      sg9550   76912\n",
       "1      sg3524  130906\n",
       "2      sg4313  173188\n",
       "3     sg21448    5313\n",
       "4     sg22543   46973\n",
       "...       ...     ...\n",
       "2232  sg28153     NaN\n",
       "2233  sg28154     NaN\n",
       "2234  sg20442     NaN\n",
       "2235   sg1777     NaN\n",
       "2236   sg1778     NaN\n",
       "\n",
       "[2237 rows x 2 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#读取销量数据\n",
    "df = pd.read_csv('sale_new.csv', usecols=[\"seriesId\",\"sum2020\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "laden-hundred",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 删除NaN数据\n",
    "df = df.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "handmade-profile",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seriesId</th>\n",
       "      <th>sum2020</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sg9550</td>\n",
       "      <td>76912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sg3524</td>\n",
       "      <td>130906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sg4313</td>\n",
       "      <td>173188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sg21448</td>\n",
       "      <td>5313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sg22543</td>\n",
       "      <td>46973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2212</th>\n",
       "      <td>sg12794</td>\n",
       "      <td>8185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2213</th>\n",
       "      <td>sg22317</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2214</th>\n",
       "      <td>sg10981</td>\n",
       "      <td>6164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2224</th>\n",
       "      <td>sg11763</td>\n",
       "      <td>8735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2226</th>\n",
       "      <td>sg25549</td>\n",
       "      <td>17164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>868 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     seriesId sum2020\n",
       "0      sg9550   76912\n",
       "1      sg3524  130906\n",
       "2      sg4313  173188\n",
       "3     sg21448    5313\n",
       "4     sg22543   46973\n",
       "...       ...     ...\n",
       "2212  sg12794    8185\n",
       "2213  sg22317     117\n",
       "2214  sg10981    6164\n",
       "2224  sg11763    8735\n",
       "2226  sg25549   17164\n",
       "\n",
       "[868 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 去除数据值为 - 的数据\n",
    "rs = df[df['sum2020']!='-']\n",
    "rs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "found-origin",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seriesId</th>\n",
       "      <th>finalPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sg9550</td>\n",
       "      <td>22.97万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sg9550</td>\n",
       "      <td>22.97万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sg9550</td>\n",
       "      <td>25.23万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sg9550</td>\n",
       "      <td>25.23万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sg9550</td>\n",
       "      <td>26.31万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14834</th>\n",
       "      <td>sg28153</td>\n",
       "      <td>暂无报价</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14835</th>\n",
       "      <td>sg28153</td>\n",
       "      <td>暂无报价</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14836</th>\n",
       "      <td>sg28153</td>\n",
       "      <td>暂无报价</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14837</th>\n",
       "      <td>sg28153</td>\n",
       "      <td>暂无报价</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14838</th>\n",
       "      <td>sg20442</td>\n",
       "      <td>39.25万</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14839 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      seriesId finalPrice\n",
       "0       sg9550     22.97万\n",
       "1       sg9550     22.97万\n",
       "2       sg9550     25.23万\n",
       "3       sg9550     25.23万\n",
       "4       sg9550     26.31万\n",
       "...        ...        ...\n",
       "14834  sg28153       暂无报价\n",
       "14835  sg28153       暂无报价\n",
       "14836  sg28153       暂无报价\n",
       "14837  sg28153       暂无报价\n",
       "14838  sg20442     39.25万\n",
       "\n",
       "[14839 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取汽车价格\n",
    "config = pd.read_csv('汽车参数_太平洋汽车_new.csv', usecols=[\"seriesId\",\"finalPrice\"])\n",
    "config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ultimate-athens",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seriesId</th>\n",
       "      <th>finalPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sg9550</td>\n",
       "      <td>22.97万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sg9550</td>\n",
       "      <td>22.97万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sg9550</td>\n",
       "      <td>25.23万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sg9550</td>\n",
       "      <td>25.23万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sg9550</td>\n",
       "      <td>26.31万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14828</th>\n",
       "      <td>sg4194</td>\n",
       "      <td>184.61万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14829</th>\n",
       "      <td>sg4194</td>\n",
       "      <td>184.61万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14830</th>\n",
       "      <td>sg4194</td>\n",
       "      <td>210.26万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14831</th>\n",
       "      <td>sg4194</td>\n",
       "      <td>210.26万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14838</th>\n",
       "      <td>sg20442</td>\n",
       "      <td>39.25万</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14652 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      seriesId finalPrice\n",
       "0       sg9550     22.97万\n",
       "1       sg9550     22.97万\n",
       "2       sg9550     25.23万\n",
       "3       sg9550     25.23万\n",
       "4       sg9550     26.31万\n",
       "...        ...        ...\n",
       "14828   sg4194    184.61万\n",
       "14829   sg4194    184.61万\n",
       "14830   sg4194    210.26万\n",
       "14831   sg4194    210.26万\n",
       "14838  sg20442     39.25万\n",
       "\n",
       "[14652 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 去除暂无报价的汽车数据\n",
    "config = config[config['finalPrice']!='暂无报价']\n",
    "config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "retained-trinity",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seriesId</th>\n",
       "      <th>finalPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sg9550</td>\n",
       "      <td>22.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sg9550</td>\n",
       "      <td>22.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sg9550</td>\n",
       "      <td>25.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sg9550</td>\n",
       "      <td>25.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sg9550</td>\n",
       "      <td>26.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14828</th>\n",
       "      <td>sg4194</td>\n",
       "      <td>184.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14829</th>\n",
       "      <td>sg4194</td>\n",
       "      <td>184.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14830</th>\n",
       "      <td>sg4194</td>\n",
       "      <td>210.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14831</th>\n",
       "      <td>sg4194</td>\n",
       "      <td>210.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14838</th>\n",
       "      <td>sg20442</td>\n",
       "      <td>39.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14652 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      seriesId  finalPrice\n",
       "0       sg9550       22.97\n",
       "1       sg9550       22.97\n",
       "2       sg9550       25.23\n",
       "3       sg9550       25.23\n",
       "4       sg9550       26.31\n",
       "...        ...         ...\n",
       "14828   sg4194      184.61\n",
       "14829   sg4194      184.61\n",
       "14830   sg4194      210.26\n",
       "14831   sg4194      210.26\n",
       "14838  sg20442       39.25\n",
       "\n",
       "[14652 rows x 2 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 去掉单位并转型为float\n",
    "config['finalPrice'] = config['finalPrice'].str.replace(\"万\",'').astype(float)\n",
    "config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fifty-accessory",
   "metadata": {},
   "outputs": [],
   "source": [
    "lastRs = pd.merge(rs,config,on=\"seriesId\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "norman-disclaimer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seriesId</th>\n",
       "      <th>sum2020</th>\n",
       "      <th>finalPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sg9550</td>\n",
       "      <td>76912</td>\n",
       "      <td>22.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sg9550</td>\n",
       "      <td>76912</td>\n",
       "      <td>22.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sg9550</td>\n",
       "      <td>76912</td>\n",
       "      <td>25.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sg9550</td>\n",
       "      <td>76912</td>\n",
       "      <td>25.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sg9550</td>\n",
       "      <td>76912</td>\n",
       "      <td>26.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5791</th>\n",
       "      <td>sg25549</td>\n",
       "      <td>17164</td>\n",
       "      <td>11.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5792</th>\n",
       "      <td>sg25549</td>\n",
       "      <td>17164</td>\n",
       "      <td>11.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5793</th>\n",
       "      <td>sg25549</td>\n",
       "      <td>17164</td>\n",
       "      <td>12.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5794</th>\n",
       "      <td>sg25549</td>\n",
       "      <td>17164</td>\n",
       "      <td>12.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5795</th>\n",
       "      <td>sg25549</td>\n",
       "      <td>17164</td>\n",
       "      <td>12.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5796 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     seriesId sum2020  finalPrice\n",
       "0      sg9550   76912       22.97\n",
       "1      sg9550   76912       22.97\n",
       "2      sg9550   76912       25.23\n",
       "3      sg9550   76912       25.23\n",
       "4      sg9550   76912       26.31\n",
       "...       ...     ...         ...\n",
       "5791  sg25549   17164       11.81\n",
       "5792  sg25549   17164       11.81\n",
       "5793  sg25549   17164       12.69\n",
       "5794  sg25549   17164       12.93\n",
       "5795  sg25549   17164       12.93\n",
       "\n",
       "[5796 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lastRs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cosmetic-reporter",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x26398852c88>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(lastRs.finalPrice[:100],lastRs.sum2020[:100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "interested-metadata",
   "metadata": {},
   "outputs": [],
   "source": [
    "cleanData = lastRs.drop_duplicates(subset=['sum2020'],keep='last')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "threatened-venture",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x26398b70da0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(cleanData.finalPrice,cleanData.sum2020)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "insured-filing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seriesId</th>\n",
       "      <th>sum2020</th>\n",
       "      <th>finalPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>sg9550</td>\n",
       "      <td>76912</td>\n",
       "      <td>28.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>sg3524</td>\n",
       "      <td>130906</td>\n",
       "      <td>44.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>sg4313</td>\n",
       "      <td>173188</td>\n",
       "      <td>73.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>sg21448</td>\n",
       "      <td>5313</td>\n",
       "      <td>52.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>sg22543</td>\n",
       "      <td>46973</td>\n",
       "      <td>29.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5743</th>\n",
       "      <td>sg12794</td>\n",
       "      <td>8185</td>\n",
       "      <td>6.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5746</th>\n",
       "      <td>sg22317</td>\n",
       "      <td>117</td>\n",
       "      <td>11.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5747</th>\n",
       "      <td>sg10981</td>\n",
       "      <td>6164</td>\n",
       "      <td>11.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5750</th>\n",
       "      <td>sg11763</td>\n",
       "      <td>8735</td>\n",
       "      <td>8.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5795</th>\n",
       "      <td>sg25549</td>\n",
       "      <td>17164</td>\n",
       "      <td>12.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>816 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     seriesId sum2020  finalPrice\n",
       "13     sg9550   76912       28.17\n",
       "23     sg3524  130906       44.61\n",
       "48     sg4313  173188       73.38\n",
       "49    sg21448    5313       52.51\n",
       "61    sg22543   46973       29.88\n",
       "...       ...     ...         ...\n",
       "5743  sg12794    8185        6.45\n",
       "5746  sg22317     117       11.58\n",
       "5747  sg10981    6164       11.48\n",
       "5750  sg11763    8735        8.11\n",
       "5795  sg25549   17164       12.93\n",
       "\n",
       "[816 rows x 3 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleanData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aware-biography",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
